Around 1996, when I attended my first programming classes, C++ was the language of choice if you wanted to have a job in this industry. The Internet was young and not as widely available as it is now. Ruby and Python were still in their infancy. Now, in 2018, both have evolved and matured well enough to be in the top 10 most in-demand programming languages. In this article, I’m going to highlight features and contrasts between Python and Ruby.
As reported on Issue #228, if scout_apm is disabled on a node via the configuration monitor = false, we don't intend to install any instruments, but a few snuck in anyway. Since the rest of the agent isn't running, they (slowly but steadily) built up recorded info, but didn't purge it, causing a slow memory leak that became clear over the course of a week or two. We've stopped the offending instruments from installing themselves when Scout is disabled.
When Ruby support on AWS Lambda was announced yesterday, I was so excited about it that I had to try it right away. We've been using Lambda for a while at Honeybadger, and I have longed to be able to write our functions in Ruby. Having played with the new Ruby support for a few hours, I'm feeling confident we'll be spending less time with Node, Go, and Python. :)
The GitHub Readme describes Falcon as, "... *a multi-process, multi-fiber rack-compatible HTTP server ... Each request is executed within a lightweight fiber and can block on up-stream requests without stalling the entire server process." The gist: Falcon aims to increase throughput of web applications by using Ruby’s Fibers to be able to continue serving requests while other requests are waiting on IO (ActiveRecord queries, network requests, file read/write, etc).
Our new RubyGems.org public dataset is now available — use it to analyze global download traffic of all gems hosted on RubyGems!
Last week we released version 4.0.0 of the honeybadger Ruby gem. This release includes a long-awaited feature which makes it even easier to customize your error reports before they are sent to Honeybadger. We also did some much-needed refactoring, and made a few removals and deprecations for good measure. Don't worry, though—most of the API remains unchanged, so upgrading should be a relatively painless process for most users.
In a previous post, we walked through how you can configure logging for Rails applications, create custom logs, and use Lograge to convert the standard Rails log output into a more digestible JSON format. In this post, we will show how you can forward these application logs to Datadog and keep track of application behavior with faceted log search and analytics, custom processing pipelines, and log-based alerting.
Logging is an important part of understanding the behavior of your applications. Your logs contain essential records of application operations including database queries, server requests, and errors. With proper logging, you always have comprehensive, context-rich insights into application usage and performance. In this post, we’ll walk through logging options for Rails applications and look at some best practices for creating informative logs.
Most scientific papers are unlikely to change your day-to-day approach as a Rails web developer. How not to structure your database-backed web applications: a study of performance bugs in the wild Yang et al., ICSE’18 is the exception to that rule. This study examined 12 popular, mature, opensource Rails apps for ActiveRecord performance anti-patterns. And boy, did they find some issues.
In this post, we’ll show you how to use Datadog to collect metrics, request traces, and logs from a Rails application running on Passenger, Apache, and MySQL. (Though we will focus on these integrations in this guide, you can also monitor NGINX servers and PostgreSQL database systems with Datadog.)
